EEE436

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EEE436

• DIGITAL COMMUNICATION
• Coding

En. Mohd Nazri Mahmud MPhil (Cambridge, UK) BEng (Essex, UK) nazriee_at_eng.usm.my Room 2.14
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Channel Coding
Why? To increase the resistance of digital
communication systems to channel noise via error
control coding How? By mapping the incoming data
sequence into a channel input sequence and
inverse mapping the channel output sequence into
an output data sequence in such a way that the
overall effect of channel noise on the system is
minimised Redundancy is introduced in the
channel encoder so as to reconstruct the original
source sequence as accurately as possible.
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Error Control Coding
Error control for data integrity may be exercised
by means of forward error correction (FEC).
The discrete source generates information in the
form of binary symbols. The channel encoder
accepts message bits and adds redundancy to
produce encoded data at higher bit rate. The
channel decoder uses the redundancy to decide
which message bits were actually
transmitted. What is the implication?
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The implication of Error Control Coding
Addition of redundancy implies the need for
increased transmission bandwidth It also adds
complexity in the decoding operation Therefore,
there is a design trade-off in the use of
error-control coding to achieve acceptable error
performance considering bandwidth and system
complexity.

• Types of Error Control Coding
• Block codes
• Convolutional codes

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Block Codes
Usually in the form of (n,k) block code where n
is the number of bits of the codeword and k is
the number of bits for the binary message To
generate an (n,k) block code, the channel encoder
accepts information in successive k-bit
blocks For each block add (n-k) redundant bits to
produce an encoded block of n-bits called a
code-word The (n-k) redundant bits are
algebraically related to the k message bits The
channel encoder produces bits at a rate called
the channel data rate, R0
Where Rs is the bit rate of the information
source and n/k is the code rate
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Forward Error-Correction (FEC) The channel
encoder accepts information in successive k-bit
blocks and for each block it adds (n-k) redundant
bits to produce an encoded block of n-bits called
a code-word. The channel decoder uses the
redundancy to decide which message bits were
actually transmitted. In this case, whether the
decoding of the received code word is successful
or not, the receiver does not perform further
processing. In other words, if an error is
detected in a transmitted code word, the receiver
does not request for retransmission of the
corrupted code word.

• Automatic-Repeat Request (ARQ) scheme
• Upon detection of error, the receiver requests a
  repeat transmission of the corrupted code word
• There are 3 types of ARQ scheme
• Stop-and-Wait
• Continuous ARQ with pullback
• Continuous ARQ with selective repeat

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Types of ARQ scheme

• Stop-and-wait
• A block of message is encoded into a code word
  and transmitted
• The transmitter stops and waits for feedback
  from the receiver either an acknowledgement of a
  correct receipt of the codeword or a
  retransmission request due to error in decoding.
• The transmitter resends the code word before
  moving onto the next block of message
• What is the implication of this?

Idle time during stop-and-wait is wasted and will
reduce the data throughput
Any idea to overcome this?
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Types of ARQ scheme

• Continuous ARQ with pullback (or go-back-N)
• Allows the receiver to send a feedback signal
  while the transmitter is sending another code
  word
• The transmitter continues to send a succession of
  code words until it receives a retransmission
  request.
• It then stops and pulls back to the particular
  code word that was not correctly decoded and
  retransmits the complete sequence of code words
  starting with the corrupted one.
• What is the implication of this?

Code words that are successfully decoded are also
retransmitted. This is a waste of resources
Any idea to overcome this?
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• Continuous ARQ with selective repeat
• Retransmits the code word that was incorrectly
  decoded only.
• Thus, eliminates the need for retransmitting the
  successfully decoded code words.

ARQ schemes (a) stop-and-wait (b) go-back (c)
selective repeat Figure 13.1-7
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Linear Block Codes An (n,k) block code indicates
that the codeword has n number of bits and k is
the number of bits for the original binary
message A code is said to be linear if any two
code words in the code can be added in modulo-2
arithmetic to produce a third code word in the
code
Code Vectors Any n-bit code word can be
visualised in an n-dimensional space as a vector
whose elements having coordinates equal the bits
in the code word For example a code word 101 can
be written in a row vector notation as (1 0 1)
Matrix representation of block codes The code
vector can be written in matrix form A block of
k message bits can be written in the form of
1-by-k matrix Modulo-2 operations The encoding
and decoding functions involve the binary
arithmetic operation of modulo-2 Rules for
modulo-2 operations are..
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Modulo-2 operations The encoding and decoding
functions involve the binary arithmetic operation
of modulo-2 Rules for modulo-2 operations
are Modulo-2 addition 0 0 0 1 0
1 0 1 1 1 1 0 Modulo-2
multiplication 0 x 0 0 1 x 0 0 0 x 1
0 1 x 1 1
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Linear Block Code Example The Repetition
Code The additional (redundancy) bits (n-k) are
identical to k
Example A (5,1) repetition code. The original
binary message has 1 bit. (5-14) bits are added
to the binary message to form a code word and the
4 additional bits are identical to the 1 bit
binary message. So, you have 2 code words either
11111 or 00000. In the case of error, 1 will
changed to 0 and/or vice versa and the decoder
will know that it has wrongly received a code
word.
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Parity-check Codes Codes are based on the notion
of parity. The parity of a binary word is said to
be even when the word contains and even number of
1s and odd parity when it has odd number of
1s. A group of n-bits codewords are constructed
from a group of n-1 message bits. One check bit
is added to the n-1 message bits such that all
the codewords have the same parity When the
received codeword has different parity, we know
that an error has occurred
Example n3 and even parity The binary message
are 00,01,10,11 The check bit is added such that
all the code words have even parity So, the
resulting code words are 000,011,101 and 110
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Systematic Block Codes Codes in which the
message bits are transmitted in an unaltered form.
Example Consider an (n,k) linear block
code There are 2k number of distinct message
blocks and 2n number of distinct code
words Let m0,m1,.mk-1 constitute a block of
k-bits binary message By applying this sequence
of message bits to a linear block encoder, it
adds n-k bits to the binary message Let
b0,b1,.bn-k-1 constitute a block of n-k-bits
redundancy This will produce an n-bits code
word Let c0,c1,.cn-1 constitute a block of
n-bits code word Using vector representation
they can be written in a row vector notation
respectively as (c0 c1 . cn ) , (m0 m1
. mk-1 ) and (b0 b1 . bn-k-1 )
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Systematic Block Codes
Using matrix representation, we can define c,
the 1-by-n code vector c0 c1 . cn m,
the 1-by-k message vector m0 m1 . mk-1 b,
the 1-by-(n-k) parity vector b0 b1 . bn-k-1
With a systematic structure, a code word is
divided into 2 parts. 1 part occupied by the
binary message only and the other part by the
redundant (parity) bits.
The (n-k) left-most bits of a code word are
identical to the corresponding parity bits The k
right-most bits of a code word are identical to
the corresponding message bits
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Systematic Block Codes
In matrix form, we can write the code vector,c as
a partitioned row vector in terms of vectors m
and b cb m Given a message vector m, the
corresponding code vector, c for a systematic
linear (n,k) block code can be obtained by a
matrix multiplication cm.G Where G is the
k-by-n generator matrix.
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Systematic Block Codes The generator matrix, G
G, the k-by-n generator matrix has the general
structure G Ik P Where Ik is the k-by-k
identity matrix and P is the k-by-(n-k)
coefficient matrix
1 0 . 0
0 1 . 0

0 0 . 1
P00 P01 .. P0,n-k-1
P10 P11 .. P1,n-k-1

Pk-1, 0 Pk-1,1 .. Pk-1,n-k-1
Ik
P
The identity matrix simply reproduces the message
vector for the first k elements of c The
coefficient matrix generates the parity vector,b
via bm.P The elements of P are found via
research on coding.
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• Hamming Code
• A type of (n, k) linear block codes with the
  following parameters
• Block length, n 2m - 1
• Number of message bits, k 2m m -1
• Number of parity bits n-km
• m gt 3

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Hamming Code Example A (7,4) Hamming code
with the following parameters n7 k4, m7-43
The k-by-(n-k) (4-by-3) coefficient matrix, P
The generator matrix, G is, G
1 1 0
0 1 1
1 1 1
1 0 1
P
1 1 0 1 0 0 0
0 1 1 0 1 0 0
1 1 1 0 0 1 0
1 0 1 0 0 0 1
G
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Hamming Code Example The parity vector,b is
generated by bm.P For a given block of message
bits m (m1 m2 m3 m4), we can work out the
parity vector, b and hence the code word, c mG
for the (7,4) Hamming Code. Exercise Try to
work out the codewords for the (7,4) Hamming
Code.
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Cyclic Codes A subclass of linear codes having a
cyclic structure. The code vector can be
expressed in the form c ( cn-1 cn-2 c1 c0
) A new code vector in the code can be produced
by cyclic shifting of another code vector. For
example, a cyclic shift of all n bits one
position to the left gives
c ( cn-2 cn-3 c1 c0 cn-1)
A second shift produces another code vector, c
c ( cn-3 cn-4 c1 c0 cn-1 cn-2)
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Cyclic Codes The cyclic property can be treated
mathematically by associating a code vector, c
with the code polynomial, c(X) c(X) c0 c1X
c2X2cn-1Xn-1 The power of X denotes the
positions of the codeword bits. The coefficients
are either 1s and 0s.
An (n,k) cyclic code is defined by a generator
polynomial, g(X) g(X) Xn-k gn-k-1Xn-k-1
. g1X 1 The coefficient g are such that
g(X) is a factor of Xn 1
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• Cyclic Codes Encoding Procedure
• To encode an (n,k) cyclic code
• Multiply the message polynomial , m(X) by Xn-k
• Divide Xn-k.m(X) by the generator polynomial,
  g(X) to obtain the remainder polynomial, b(X)
• 3. Add b(X) to Xn-k.m(X) to obtain the code
  polynomial

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Cyclic Codes - Example The (7,4) Hamming Code
For message sequence 1001 The message
polynomial, m(X) 1 X3 1. Multiply by Xn-k
(X3) gives X3 X6 2. Divide by the generator
polynomial, g(X) that is a factor of Xn 1
For the (7,4) Hamming code is defined by its
generator polynomials, g(X) that are factors of
X7 1 With n 7, we can factorize X7 1 into
three irreducible polynomials X7 1 (1
X)(1 X2 X3)(1 X X3)
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Cyclic Codes - Example For example we choose the
generator polynomial, 1 X X3 and perform the
division we get the remainder, b(X) as X2
X 3. Add b(X) to obtain the code polynomial,
c(X) c(X) X X2 X3 X6 So the
codeword for message sequence 1001 is 0111001
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Cyclic Codes Exercise Find the codeword for
(7,4) cyclic Hamming Code using the generator
polynomial, 1 X X3 for the message sequence
0011
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Cyclic Codes Implementation The Cyclic code is
implemented by the shift-register encoder with
(n-k) stages
Encoding starts with the feedback switch closed,
the output switch in the message bit position,
and the register initialised to the all-zero
state. The k message bits are shifted into the
register and delivered to the transmitter. After
k shift cycles, the register contains the b check
bits. The feedback switch is now opened and the
output switch is moved to the check bits to
deliver them to the transmitter.
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Cyclic Codes Implementation example The
shift-register encoder for the (7,4) Hamming Code
has (7-43) stages
When the input message is 0011, after 4 shift
cycles the redundancy bits are delivered
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Cyclic Codes Implementation Exercise The
shift-register encoder for the (7,4) Hamming Code
has (7-43) stages
When the input message is 1001, after 4 shift
cycles the redundancy bits are delivered
The check bits is 011
1
0
0
0
0
1
1
0
0
1
1
1
1
0
0
1
1
0
1
1
1
1
1
1
1
1
1
0
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Cyclic Codes Implementation Exercise The
shift-register encoder for the (7,4) Hamming Code
has (7-43) stages
When the input message is 1100?
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Code parameters

• The Hamming distance
• The Hamming distance between a pair of code
  vectors, c1 and c2 that have the same number of
  elements is defined as the number of locations in
  which their respective elements differ
• The Hamming weight
• The Hamming weight of a code vector c is defined
  as the number of nonzero elements in that code
  vector
• Equivalent to the distance between a code vector
  an an all-zero code vector
• The minimum distance
• The minimum distance of a linear block code is
  defined as the smallest Hamming distance between
  any pair of code vectors in the code.
• Equivalent to the smallest Hamming weight of the
  difference between any pair of code vectors
• Equivalent to the smallest Hamming weight of the
  nonzero code vectors in the code
• Code rate
• The ratio between the number of original message
  bits and the number of bits of the codeword
• For (n,k) code , code rate k/n.

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Code parameters

• The minimum distance of a code determines the
  error detecting and correcting capability of the
  code
• Error detection is always possible when the
  number of transmission errors in a codeword is
  less than the minimum distance so that the
  erroneous word may not be seen as another valid
  code vector
• Various degrees of error control capability
• Detect up to l errors per word , dmin gt l 1
• Correct up to t errors per word, dmin gt 2t 1
• Correct up to t errors and detect l gt t errors
  per word,
• dmin gt t l 1
• Code rate is a measure of the code efficiency